class_docstr: MVLSTM Model.
  
  Examples:
      >>> model = MVLSTM()
      >>> model.params['lstm_units'] = 32
      >>> model.params['top_k'] = 50
      >>> model.params['mlp_num_layers'] = 2
      >>> model.params['mlp_num_units'] = 20
      >>> model.params['mlp_num_fan_out'] = 10
      >>> model.params['mlp_activation_func'] = 'relu'
      >>> model.params['dropout_rate'] = 0.5
      >>> model.guess_and_fill_missing_params(verbose=0)
      >>> model.build()
docstr: :return: model default parameters.
docstr_tok: [":","return",":","model","default","parameters","."]
code: def get_default_params(cls) -> ParamTable:
      """:return: model default parameters."""
      params = super().get_default_params(
          with_embedding=True, with_multi_layer_perceptron=True
      )
      params.add(
          Param(
              name="lstm_units",
              value=32,
              desc="Integer, the hidden size in the "
              "bi-directional LSTM layer.",
          )
      )
      params.add(
          Param(
              name="dropout_rate",
              value=0.0,
              desc="Float, the dropout rate.",
          )
      )
      params.add(
          Param(
              "top_k",
              value=10,
              hyper_space=hyper_spaces.quniform(low=2, high=100),
              desc="Integer, the size of top-k pooling layer.",
          )
      )
      params["optimizer"] = "adam"
      return params
  
file_path: MatchZoo/matchzoo/models/mvlstm.py
imports: import keras
  import tensorflow
  from matchzoo.engine import hyper_spaces
  from matchzoo.engine.base_model import BaseModel
  from matchzoo.engine.param import Param
  from matchzoo.engine.param_table import ParamTable
label_desc: Use image node_id 1 for calling the MVLSTM obj's underlying member method code with example usage: obj.get_default_params(cls) and returns: params